English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Using a vocabulary that is shared across languages is common practice inMultilingual Neural Machine Translation (MNMT). In addition to its simpledesign, shared tokens play an important role in positive knowledge transfer,assuming that shared tokens refer to similar meanings across languages.However, when word overlap is small, especially due to different writingsystems, transfer is inhibited. In this paper, we define word-level informationtransfer pathways via word equivalence classes and rely on graph networks tofuse word embeddings across languages. Our experiments demonstrate theadvantages of our approach: 1) embeddings of words with similar meanings arebetter aligned across languages, 2) our method achieves consistent BLEUimprovements of up to 2.3 points for high- and low-resource MNMT, and 3) lessthan 1.0\% additional trainable parameters are required with a limited increasein computational costs, while inference time remains identical to the baseline.We release the codebase to the community.

Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine Translation